China Stocks

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

{"entity":"Tesla"}

What changed on a web page between two dates? Resolves the archived capture nearest each date (Internet Archive Wayback Machine, Common Crawl, Arquivo.pt), extracts the readable text of both, and returns a text-level diff: the segments removed and added, a similarity score, and both capture timestamps with their gaps from the requested dates. It diffs readable text, not markup, so a restyled page with the same words reads changed:false. Identical captures answer changed:false with the reason (same capture, identical content digest, or identical text), never an empty diff. A date with no capture within max_gap_days (default 30) answers status "no_capture", naming which side is missing and the nearest captures to it. Typical uses: how a privacy policy, terms of service, pricing or product page changed; whether a statement was on a page at a given time.

{"date_a":"2022-06-02","date_b":"2024-06-15","url":"https://www.ftc.gov/policy-notices/privacy-policy"}

What did a web page say on a given date? Finds the archived capture of a URL nearest that date across the Internet Archive Wayback Machine, Common Crawl and Arquivo.pt, reads it, and returns the readable text (HTML stripped) with the capture timestamp, a link to the archived copy, and the GAP between the date asked for and the capture actually used (e.g. "Captured 1.5 day(s) before the requested date"). One call replaces the find-capture, fetch-record, parse-HTML chain. If no archive holds a capture within max_gap_days (default 30) it answers status "no_capture" and names the nearest capture before and after the date with their gaps, so a distant capture is never passed off as the page on that date. Every archive's outcome is listed in sources[]; one that timed out or errored says so with its upstream status, and if all of them failed the answer is error "archives_unavailable", not no_capture. Captures are immutable and cached by capture id; served_from says whether the text came from cache or upstream. Use archive_compare to see what changed between two dates.

{"date":"2023-06-01","url":"https://www.sec.gov"}

{"date":"2023-06-01","max_chars":400,"url":"https://www.sec.gov"}

Analyst consensus estimates (盈利预测) for a Chinese A-share stock — per forecast year: consensus EPS, P/E at current price, net profit attributable to parent (归母净利润), and total operating revenue, in CNY. Answers "analyst estimates / 盈利预测 / forecast EPS for a China A-share like 000988". Source: Eastmoney datacenter (keyless).

{"symbol":"300750"}

The Chinese A-share market's daily 龙虎榜 (dragon-tiger list / billboard) for a trading day — stocks flagged for unusual price/turnover deviation, with the top seats' buy/sell/net amounts, plus (when present that day) institution-seat (机构专用) net-buy detail and that day's most-active brokerage branches (营业部) market-wide as a 'top brokerages' signal. Answers '龙虎榜', 'dragon tiger list for a date', 'which stocks were on the billboard on 2026-09-24', '机构净买入', 'institution seat net buys', 'top active brokerage seats'. Each listed stock has the reason it was flagged (EXPLANATION, e.g. daily move ≥7%), close price, change %, and billboard buy/sell/net amounts in CNY; stocks that also had institutional-seat activity that day carry institution_net_buy_cny/institution_buy_seats/institution_sell_seats. A non-trading `date` (weekend/holiday) walks back to the prior trading day automatically. Source: Eastmoney datacenter-web (keyless).

{"date":"2026-09-24"}

Per-stock daily main-capital net inflow history (主力净流入 / 超大单+大单净流入, often called '超大单净流入' in retail trackers) for a Chinese A-share stock or ETF — one row per trading day with net inflow broken down by order size: 超大单 (super-large/institutional), 大单 (large), 中单 (medium), 小单 (small), plus 主力净流入 (main = 超大单+大单combined) in both CNY amount and as a % of that day's turnover. Answers '主力净流入历史', '超大单净流入', '000768最近20个交易日的主力资金流向', 'main capital net inflow history for a China A-share stock', 'which days did big money flow into/out of a stock'. A positive net inflow means net buying pressure from that order-size bucket that day, negative means net selling. Source: Eastmoney push2his fflow/daykline (keyless).

{"code":"000768","days":20}

Daily OHLCV history (历史日线) for one or more Chinese A-share stocks over a date range — open, high, low, close, volume, and day-over-day change % per trading day. Answers '300418在2026-08-31到2026-09-03的收盘价和涨跌幅', 'daily closes for 600519 last week', 'history for 000858 and 300750 this month', '涨跌幅历史', '历史成交量'. Up to 20 comma-separated 6-digit codes. change_pct is computed from consecutive closes within the returned range (the first row has no prior close in range, so it is null); turnover amount (成交额) is not exposed by this daily-bar source, so amount_cny is reported as "not_available" rather than estimated — use ashares_quote or ashares_turnover_ranking for same-day turnover amount. Source: Tencent/gtimg fqkline (keyless).

{"adjust":"qfq","codes":"300418,300364,002131,002354,300058","end":"2026-09-03","start":"2026-08-31"}

Company-issued earnings guidance (业绩预告) for a Chinese A-share stock — the company's own forecast announcements with predicted profit range (lower/upper bounds in CNY), year-over-year change %, forecast type (略增/预增/扭亏 etc.), and the company's stated reason. Answers '业绩预测', '业绩预告', 'earnings forecast/guidance for a China A-share company like 华工科技 000988'. Most recent reports first. Source: Eastmoney datacenter (keyless).

{"symbol":"000988"}

{"limit":8,"symbol":"600519"}

Disclosed fund-share-count (基金份额) and net-asset history for a Chinese-listed ETF — period-end total shares outstanding (期末总份额), the period's subscriptions/redemptions (期间申购/赎回), and period-end net assets (期末净资产), in shares and CNY. Answers '基金份额变动', 'ETF shares outstanding history', '515050规模变动', 'how many shares outstanding does this ETF have over time'. IMPORTANT: this is NOT a daily series — Chinese ETFs publicly disclose 份额 only at quarter-end plus occasional ad-hoc material-change dates (confirmed live for 515050/561980/588170/588200), not every trading day; a `from`/`to` window with no disclosure inside it returns found:false naming the nearest disclosed dates rather than fabricating daily rows. For a daily PRICE series use ashares_daily_history; this tool is share-count/AUM only. Source: Eastmoney fund F10 archive (keyless).

{"code":"515050"}

Intraday minute bars (分钟线/分时/分钟K线) for Chinese A-share stocks/ETFs on a given trading日/交易日, with cumulative volume and turnover (累计成交量/累计成交额) computed up to a requested time of day (截至<time>, e.g. 截至11:05) — answers '515050在2026-09-01这个交易日截至11:05的累计成交量和累计成交额', '600519某个交易日截至某时刻的累计成交量和成交额', 'cumulative volume for 600519 up to 11:05 on a past trading day', 'intraday minute bars for an A-share on a specific date'. ashares_quote is realtime-only (today, right now) and ashares_daily_history is end-of-day-only (no intraday granularity) — this tool is for a PAST trading日's intraday trajectory. Returns the interval bars for that day plus cumulative_volume_shares/cumulative_turnover_cny summed from the day's open through `up_to` (or the whole day if `up_to` is omitted). `symbols` takes a bare 6-digit code (e.g. "515050") or the sh/sz/bj-prefixed form, same as ashares_quote. Bounded lookback: the upstream caps total bars at ~1950 regardless of interval, so retention shrinks as the interval shrinks — roughly 8 trading days at 1-minute, 40 at 5-minute (the default), 110 at 15-minute, a year at 30-minute, two years at 60-minute — and it slides forward daily. A `date` older than a result's `earliest_bar_date_available` is out of range for that interval; try a coarser interval, or use ashares_daily_history for daily (not intraday) history further back. Source: Sina (keyless).

{"date":"2026-09-01","symbols":"515050,600519","up_to":"11:05"}

The Chinese A-share market's LIMIT-DOWN board (跌停板) for a trading day — how many stocks hit their daily DOWN price limit (-10%, or -20% for STAR/ChiNext) and the ranked list of those stocks. Answers 'how many A-shares hit limit down today', '跌停家数', 'which stocks 跌停', 'limit-down stock count for a China A-share trading day' — the counterpart to ashares_limit_up, together giving market-wide limit-up/limit-down breadth. Each stock includes code, name, price, change % (negative), turnover (成交额), float market cap, turnover rate (换手率), seal fund (封板资金), last seal time, industry (行业), and P/E. Source: Eastmoney (keyless).

{}

{"date":"2026-10-08","limit":10}

The Chinese A-share market's LIMIT-UP board (涨停板) for a trading day — how many stocks hit their daily price limit (+10%, or +20% for STAR/ChiNext) and the ranked list of those stocks. Answers 'how many A-shares hit limit up today', 'top limit-up stocks', 'which stocks 涨停'. Each stock includes code, name, price, change %, turnover (成交额), float market cap, turnover rate (换手率), consecutive-board count (连板数 lbc), first/last seal time, seal fund (封板资金), and industry (行业). Source: Eastmoney (keyless).

{}

{"limit":10}

China A-share margin financing and securities lending balance (融资融券余额/两融余额) — the market-wide leverage gauge: aggregate financing balance (融资余额, money borrowed to buy stock), securities-lending balance (融券余额, shares borrowed to short), and their combined total (融资融券余额), for the Shanghai (SSE) and Shenzhen (SZSE) exchanges separately and combined, with day-over-day change. Answers '融资余额', '两融余额', '融资融券余额', 'margin financing balance', 'China A-share margin debt', 'how much margin debt is in the A-share market', 'margin balance day over day change'. Pass a 6-digit SZSE code (0/3/159-prefixed) in `symbol` for that stock's own 融资余额/融券余额/融资融券余额 instead of the market aggregate — SSE (6xxxxx) per-stock detail is not available via any verified keyless route, so a SH code returns a clear not_available message rather than silently substituting. Margin data publishes T+1 (today's figures appear tomorrow morning); a request for a day with nothing published yet or a non-trading day (weekend/holiday) automatically falls back to the last trading day that published, and says so. Amounts in CNY (converted from each exchange's native 元/亿元/万元 units and labelled). Source: SSE query.sse.com.cn (keyless) + SZSE www.szse.cn ShowReport CATALOGID=1837_xxpl (keyless).

{}

{"symbol":"000001"}

Overall snapshot of the Chinese A-share market — the headline index levels (Shanghai Composite, Shenzhen Component, ChiNext, STAR 50, CSI 300) with change %, day range and turnover, plus market-wide breadth as a sentiment gauge: how many stocks hit limit-up (涨停) and limit-down (跌停) today. Answers 'how is the China A-share market doing', 'Shanghai Composite today', 'A-share market snapshot at close', 'how did Chinese stocks close', '涨跌停家数', 'limit-up limit-down counts today'. For the ranked pools use ashares_limit_up / ashares_limit_down; for market-wide margin financing leverage use ashares_margin_financing. Source: Sina + Eastmoney (keyless).

{}

Real-time quote(s) for Chinese A-share stocks by 6-digit code (Shanghai 6xxxxx, Shenzhen 0xxxxx/3xxxxx, STAR 688xxx, ChiNext 30xxxx, Beijing 8xxxxx/4xxxxx). Returns name, current price, change and change %, open, previous close, day high/low, volume, turnover, and the quote timestamp. Accepts one code or a comma-separated list. Example: ashares_quote({ symbols: "600519,000858" }) for Kweichow Moutai and Wuliangye. Source: Sina (keyless).

{"symbols":"600519"}

{"symbols":"600519,000858,300750"}

China A-share INDUSTRY-SECTOR net capital inflow ranking (行业资金流向排名/板块资金流) for a trading day -- ranks Eastmoney's ~128 industry boards (行业板块) by main-capital net inflow (主力净流入), with the breakdown by order size (超大单/大单/中单/小单 net inflow in CNY), the day's change %, and each sector's single stock with the largest net inflow. Answers '今日行业资金流向排名', 'which industry sectors had the biggest net capital inflow today', '板块资金流排名', 'sector fund flow ranking', 'semiconductor sector money flow today'. This is a DIFFERENT metric from china-exchange-data's ashares_sector_flows, which is SZSE's own official per-industry TURNOVER ranking -- this tool is Eastmoney's vendor-computed NET INFLOW (主力净流入) classification, the sector-level counterpart to this pack's own per-stock ashares_capital_flow. A non-trading date (weekend/holiday, or before today's session has published -- publication usually lags a day) walks back automatically to the nearest prior trading day with data. Source: Eastmoney datacenter (keyless).

{"limit":5}

{"limit":3,"sort":"change_pct"}

Stock Connect (沪深港通) daily channel statistics: GROSS TURNOVER for Northbound (mainland A-shares bought via Hong Kong -- 沪股通/深股通/combined) and Southbound (Hong Kong-listed stocks bought by mainland investors -- 港股通沪/港股通深/combined), plus a best-effort per-stock "top traded stocks" list for Northbound. Answers "northbound stock connect turnover today", "沪深股通成交额", "how much did northbound/southbound trade today", "stock connect top traded stocks". IMPORTANT: mainland regulators (CSRC) stopped publishing DAILY net fund-flow figures (net buy/sell, daily inflow, remaining quota) for NORTHBOUND Stock Connect in August 2024 -- every Northbound channel's net_flow field says so explicitly rather than returning null as if it were real data; gross turnover is unaffected and is published daily for every channel. Southbound net flow is still disclosed by Hong Kong Exchanges and Clearing and is returned when available. A turnover-ranked top-10-stocks list for Northbound depends on an Eastmoney report that has returned a server error on every live probe during this build -- the tool still attempts it live on every call and reports plainly under top_traded_stocks.northbound whether real data came back, rather than silently omitting the field or faking rows. Source: Shanghai Stock Exchange / Shenzhen Stock Exchange / Hong Kong Exchanges and Clearing, via Eastmoney datacenter (keyless).

{}

{"date":"2026-10-16"}

Technical indicators for Chinese A-share stocks and ETFs (均线/布林/MACD/KDJ) — MA (moving averages, any windows, default MA5/MA10/MA20/MA60), BOLL (Bollinger Bands, default 20-period ×2 std dev), MACD (DIF/DEA/柱, default 12/26/9), and KDJ (K/D/J, default 9/3/3), computed from this pack's own daily OHLCV history (same source and codes as ashares_daily_history — ETF codes like 515050/588170/561980 work the same as stock codes). Answers '515050通信ETF华夏最近20个交易日MA5/MA10/MA20/BOLL/MACD/KDJ', '603986兆易创新周线MACD是否死叉', 'MA20 and BOLL for an A-share ETF', '布林带', '均线', '金叉死叉'. Internally fetches extra lookback history before `start` so the indicators are warmed up (MA60/MACD need ~60+ trading days of prior data) — only the requested [start, end] range is returned. MACD histogram uses the common Chinese charting convention 柱=2×(DIF−DEA); KDJ smoothing is the standard 3/3 recursive form seeded at 50. Up to 5 comma-separated codes per call (indicator math is heavier than a plain OHLCV pull). Source: Tencent/gtimg fqkline (keyless), same as ashares_daily_history.

{"codes":"515050","end":"2026-09-22","indicators":["ma","boll","macd","kdj"],"start":"2026-09-01"}

Market-wide ranking of Chinese A-share stocks by turnover / 成交额排名, 成交额前30名, 成交量排名, or 涨跌幅排名 — the whole board (not specific codes), sorted server-side and returned as a ranked list. Answers 'A股成交额前30名', 'top 30 A-shares by turnover today', 'A股成交量排名', 'A-share market-wide ranking by amount/volume/change %', 'which A-shares traded the most today'. Each row has code, name, price, change %, volume (shares), turnover in CNY (成交额), turnover rate %, and industry (行业/板块, when resolvable). Omit `date` (or pass today) for the LIVE board (Sina, real-time). Pass a PAST trading day in `date` for that day's ranking instead of today's — this uses a different, Eastmoney-sourced historical path and only supports `sort:'changepercent'` (涨跌幅排名): no keyless bulk source for a PAST day's turnover amount or volume ranking was found (Sina's live board is the only amount/volume source), so `date` + `sort:'amount'|'volume'` throws a clear error naming that gap rather than silently substituting today's board. A non-trading `date` (weekend/holiday) walks back to the prior trading day automatically.

{"board":"hs_a","limit":30,"order":"desc","sort":"amount"}

{"limit":10,"sort":"changepercent"}

{"date":"2026-09-22","limit":10,"sort":"changepercent"}

PREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring authoritative structured data with citations. Routes the question to the right one of 7,181 tools across 1864 verified sources, fills arguments, returns the structured answer with stable pipeworx:// citation URIs. Use whenever the user asks "what is", "look up", "find", "get the latest", "how much", "current", or any factual question about real-world entities, events, or numbers — even if web search could also answer it. Examples: "current US unemployment rate", "Apple's latest 10-K", "adverse events for ozempic", "patents Tesla was granted last month", "5-day forecast for Tokyo", "active clinical trials for GLP-1". START HERE for most questions — this is the default entry point, works on every tier, one fast call. Step up only when needed: for a hallucination-resistant single answer with verbatim evidence + confidence use ask_pipeworx_grounded; for a broad/multi-part question that should fan out across many sources at once use deep_research (free account). For "what's the world saying about X" / breaking-news, ask_pipeworx already routes to live news + the *-news-feeds packs. Optionally pass packs:["slug","slug"] to restrict routing to specific sources ("answer this using THESE packs"); omit it for full-catalog routing.

{"question":"What was Apple's revenue in 2024?"}

{"question":"Any recent SEC filings for $NVDA?"}

{"question":"Current price of bitcoin"}

Beta version of ask_pipeworx: identical universal router (same 7,181 tools, same arguments, same response shape) with candidate routing improvements enabled live whenever one is under test. No candidate is active right now (the last was retired on outcome evidence 2026-07-26), so this currently matches ask_pipeworx exactly. Use it exactly like ask_pipeworx when you want the newest routing; results are compared against the stable router to decide what merges. Falls back to nothing — this IS a full working router, just the experimental edge.

{"question":"What is the current US unemployment rate?"}

Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 7,181 across 1864 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. An "llm_error" also carries refusal_cause ("wall" = our time budget ended before any model call was made; "route"|"threw"|"unparseable"|"wall_after_failure" = a model call failed); refusal_reason itself is unchanged. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

{"question":"What was Apple's fiscal 2023 revenue?"}

Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-kristi-noem-win-the-2028-republican-presidential-nomination"), a polymarket.com URL, or a question text. A dated slug ("...-by-june-30-2026") still resolves after it settles — the resolver retries a closed-market lookup rather than giving up on the bare one — so either dated or undated works; prefer whichever slug you actually have. The tool resolves the market, classifies the bet, fans out to category-specific data packs in parallel, and returns an evidence packet + simple market-vs-model comparison. Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z". CLASSIFIERS: crypto_price, fed_rate, geopolitical, sports, sports_championship, drug_approval, election_candidate, tech_launch, space_launch, corporate, corporate_earnings, corporate_event, public_figure_speech, weather, other. FAN-OUT EXAMPLES: BTC bet → coingecko + fred + gdelt+gnews; Fed bet → fred (DFEDTARU + EFFR + CPIAUCSL) + kalshi_macro (KXFED implied probs) + recent_fed_actions (federal-register rules, last 365d); Hormuz bet → imf_portwatch + airspace + gdelt; Yankees WS → mlb_stats_standings + parent_event partition + news; hottest-year bet → climate_projection_nyc + gistemp_latest (NASA global anomaly, rank since 1880) + news; NVDA-vs-AAPL → finnhub get_quote + edgar shares-outstanding (derived market cap) + edgar filings + news. RESPONSE SHAPES: result.market carries best_bid/best_ask/spread_pp/liquidity/price_change_1h/1d/1w; result.analysis carries model_probability/edge_pp/kelly_fraction_half when a closed-form model fires PLUS a 24h-move warning ("Market moved X.Xpp in 24h, comparable to model edge — your edge may already be priced in") when relevant; result.evidence is keyed by source. RESOLVER CONTRACT: result.market_match_confidence ∈ {high, medium, low, none}, market_match_score (0-1 token-overlap), market_match_alternatives[] (other candidate markets th…

{"market":"will-kristi-noem-win-the-2028-republican-presidential-nomination"}

{"market":"https://polymarket.com/event/will-kristi-noem-win-the-2028-republican-presidential-nomination"}

Cheap categorization: given a company and a list of candidate labels, decide which labels apply — the most common GTM/AI usage shape (tagging a list by industry, ICP fit, etc.), one small model call. Same evidence contract as research_row: each label comes back {label, applies, evidence_url, quote, status: found|inferred|not_established} — "found" only when the model's quote is independently verified as a verbatim substring of the text used. Pass text directly (cheapest — no fetch) or entity.domain to fetch the homepage once as a fallback. No text and no domain means every label is not_established with no model call made.

{"entity":{"domain":"stripe.com","name":"Stripe"},"labels":["healthcare","fintech","logistics"]}

Index VALUATION for a China Securities Index Co. (CSI) or SSE index code: the latest trading day's P/E and dividend yield plus roughly the last 20 trading days, dated. Answers "CSI Dividend Index 000922 latest P/E and dividend yield", "SSE Dividend 000015 valuation", "H30269 dividend low volatility index P/E", "930955 红利低波100 市盈率 股息率", "中证红利指数估值". Returns four published figures per day: P/E1 (total shares), P/E2 (calculation shares), D/P1 (dividend yield on total shares, %) and D/P2 (dividend yield on calculation shares, %). NOT in this source, and never estimated: P/B (市净率) and valuation percentile / history rank (估值分位) -- the response lists them under not_provided. Code is case-sensitive upstream: numeric codes as 6 digits (000922, 000300, 930955), letter-prefixed ones with an upper-case letter (H30269). An unknown or unpublished code returns found:false. Source: China Securities Index Co., daily index indicators.

{"index_code":"000922"}

{"days":5,"index_code":"H30269"}

TYPED, DETERMINISTIC financial facts for a US public company for an EXPLICITLY NAMED reporting period — "Apple revenue for fiscal 2023", "Walmart net income FY2026 Q3", "Microsoft cash at the end of fiscal 2024". PREFER OVER entity_profile / get_company_financials whenever the period matters: those answer "the most recent figures" and will happily hand back FY2025 when you asked about FY2019, and neither separates a discrete quarter from a year-to-date figure. This one refuses instead — it NEVER substitutes the latest period for the period requested, NEVER returns 0 for missing data, NEVER lets a 9-month YTD number answer a quarterly question, and NEVER converts a currency. Every answer carries the exact us-gaap concept it came from, what that concept MEASURES (NetIncomeLoss excludes non-controlling interests, ProfitLoss includes them — not synonyms), the accession number and a link to the filing on sec.gov, the restatement trail of any superseded figures, and a contract + derivation version to pin against. Fiscal periods are the FILER'S OWN, anchored on their fiscal-year end, so Walmart's year ending 2026-01-31 is FY2026 and Apple's ending 2025-09-27 is FY2025. Attributes: revenue, net_income, cash, gross_profit, operating_income, eps_diluted, capex, operating_cash_flow, long_term_debt, shares_outstanding (the dei COVER-PAGE share count, so its period end is the cover date, after the quarter closed — that is the most current count the filing carries). Units are not all USD: shares_outstanding is in `shares` with currency null, eps_diluted in `USD/shares`. A discrete quarter the filer never filed — every Q4, and Q2/Q3 of cash-flow lines, which are filed year-to-date — is DERIVED as YTD(q) minus YTD(q-1) and comes back with `reported: false`, the subtraction named in `derivation` (fy_minus_9m / ytd_9m_minus_6m / ytd_6m_minus_3m) and both legs with their filings in `derived_from`; test `reported` if you will only accept a figure lifted from a filing. Per-share and ba…

{"attribute":"revenue","company":"AAPL","period":{"fiscal_year":2023,"type":"annual"}}

{"attribute":"shares_outstanding","company":"TSLA","period":{"fiscal_quarter":2,"fiscal_year":2026,"type":"quarterly"}}

{"attribute":"capex","company":"TSLA","period":{"fiscal_quarter":2,"fiscal_year":2026,"type":"quarterly"}}

{"attribute":"net_income","company":"TSLA","period":{"fiscal_quarter":4,"fiscal_year":2025,"type":"quarterly"}}

Search the NATIONAL BUSINESS REGISTERS by company name and get the register's own id back — a Companies House number, a Norwegian organisasjonsnummer, a Finnish business id, a SIREN, a Japanese corporate number, an Estonian registry code, a CNPJ, or an LEI. Use this when you want the OFFICIAL REGISTERED ENTITY and the country is not the US, or when you do not know which country it is. Registers searched: Companies House (GB), Enhetsregisteret (NO), PRH/YTJ (FI), Sirene (FR), 法人番号公表サイト (JP), RIK e-Business Register (EE), and the GLEIF LEI crosswalk, which reaches jurisdictions we have no register pack for (DE, SE, AU, SG...). THIS TOOL REFUSES TO GUESS, and that is its point: when two different companies match a name equally well it answers `match_status: "ambiguous"` with BOTH, rather than naming one. A one-word trading name usually IS ambiguous — "Bolt", "Apple", "Next", "Sea", "DNB" each match a well-known group AND unrelated micro-entities whose registered name is exactly that word — so pass `country` when you know it. Every response names EVERY register it asked under `searched[]`, with that register's own as-of date and row count, and a register that could not be reached carries an `error` there rather than reading as zero rows: "we could not ask Companies House" is never the same answer as "this company is not in Britain". `related[]` holds entities that collapsed INTO the match — the same company seen through another register, or another country's arm sharing its LEI — so a multinational returns one answer rather than tying with itself. NOT A PEOPLE SEARCH: sole traders and other natural persons are excluded on the publisher's own legal-form field, never guessed from the shape of a name. For US public companies prefer resolve_entity (CIK + ticker + FIGI); the two share this same matcher, and resolve_entity returns these register fields too.

{"country":"NO","name":"Equinor ASA"}

{"country":"JP","name":"株式会社花王"}

{"country":"EE","name":"Bolt"}

"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + operating income + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

{"type":"company","values":["AAPL","MSFT"]}

ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1864 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 7,181 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

{"depth":"quick","question":"What is the current US unemployment rate and how has it changed over the past year?"}

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

{"query":"look up FDA drug approvals"}

{"query":"analyze housing market trends"}

"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO patents, federal contracts (USAspending), FDA-licensed biologics (Purple Book), H-1B hiring (DOL LCA), news and GLEIF, and returns: cik + company_name (+ resolved_from/resolved_to when `value` was a name); recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (the latest ANNUAL Revenues, NetIncomeLoss, Cash, diluted EPS, capex and operating cash flow, each with its period_end, SEC form and the accession of the filing it came from — plus EntityCommonStockSharesOutstanding, which is the newest COVER-PAGE share count the filer has published rather than an annual one, so its period_end is a cover date after the quarter closed and it is the right per-share denominator; a filer tagging a fallback concept appears under the name of that tag instead, e.g. PaymentsToAcquireProductiveAssets instead of PaymentsToAcquirePropertyPlantAndEquipment); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); federal_contracts (USAspending awards where the company is the recipient); fda_products (FDA-licensed biologics — vaccines, cell/gene therapies — from the Purple Book; a company with only small-molecule/generic drugs will show none here, that is expected, not a failure); hiring (H-1B sponsorship volume + salary range from DOL LCA filings); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. `sources_used` / `sources_failed` say which of these actually returned data for THIS company — an empty section is a real "no data", not a bug. `sources_skipped` is the third state: a leg we deliberately did NOT run, each e…

{"type":"company","value":"AAPL"}

Delete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.

{"key":"user_research_topic"}

Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.

{"url":"https://pipeworx.io"}

Prices Kalshi daily high-temperature markets against the NWS forecast for the market's OWN settlement station, and measures whether that forecast actually beats the market. Two modes. LIVE (default): returns the full strike ladder for one city and settlement date with market_prob (mid), forecast_prob, and edge_pp per strike, plus the settlement clause verbatim. BACKTEST (`backtest_days: N`): scores an archived gridded forecast against the market on settled days and returns brier_market vs brier_forecast with a plain-English `verdict`, so the edge is MEASURED rather than asserted. READ THE WARNINGS — they are not boilerplate. (1) These markets DO NOT settle on the NWS. They settle on The Weather Company (weather.com) at a Kalshi station code such as CLINYC, which the response quotes verbatim; so part of every edge_pp is NWS-vs-Weather-Company disagreement about the same day at the same station, which is not mispricing and not tradeable. `settlement_vs_forecast_basis_f` from backtest mode is that part as a number. (2) The station is DERIVED from the settlement clause, never from the city name: Chicago settles at MIDWAY and New York at CENTRAL PARK, so a city-centre forecast would misprice a whole ladder. A station that cannot be resolved yields rows with no forecast and a reason, never a guessed coordinate. (3) forecast_prob assumes a normal distribution around the NWS high whose width is ASSUMED, not fitted (stated in `distribution_assumption`) — run backtest mode to see whether it is calibrated. (4) edge_pp is gross: no Kalshi fees, no bid-ask. MEASURED RESULT, AND IT IS NOT THE FLATTERING ONE: on the first backtest (KXHIGHNY, 13 settled days to 2026-09-11, 58 market observations) the MARKET beat the forecast — Brier 0.1008 for the market against 0.1594 for the archived gridded forecast, lower being better. So on that sample there is NO forecast edge to sell, and a large edge_pp is more likely to be the model disagreeing with a better-informed market than an opport…

{"city":"nyc"}

{"city":"chicago"}

{"backtest_days":14,"series_ticker":"KXHIGHNY"}

List the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.

Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. Filing without an account returns a `claim_token`; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.

{"message":"Fleet #2184 smoke test: verifying pipeworx_feedback example call returns non-empty.","type":"other"}

What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.

{"window":"7d"}

Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FEES: every opportunities[] row and partition_check.arbitrage carry edge_pp_gross (== gap_pp / overround_pp), fees_pp, edge_pp_net, net_positive, plus polymarket_fee_pp, fee_basis and fee_categories[]. BOTH cost components are modeled: Polymarket's own per-category TAKER FEE (fee = shares × rate × p × (1-p), rates crypto 0.07 / sports-economics-culture-weather-other 0.05 / finance-politics-mentions-tech 0.04, geopolitics and world events fee-free; verified against Polymarket's own…

{"topic":"Fed rate decision"}

Edge persistence and decay telemetry built from daily polymarket_edges snapshots. Answers "how long has this edge existed and is it shrinking?" — a fresh wide edge and a 3-week-old wide edge are different trades (the latter is wide for a reason nobody is willing to take). Args: days (lookback, default 14, max 30), window (snapshot family, default "1wk"). RESPONSE: tracked[] = every opportunity in the LATEST snapshot with its full edge_pp_net time-series across prior snapshots, first_seen, trend (new | widening | stable | decaying) and decay_pp_per_day (both computed on |edge_pp_net| — the value itself is signed by trade direction, negative = SELL YES); expired[] = opportunities that appeared in earlier snapshots but are GONE from the latest (closed, resolved, or arbed away) with their lifespan_days — the median lifespan is your competition clock; snapshot_dates[] = which days actually have data (snapshots are written when polymarket_edges runs on a cache-miss, so gaps mean nobody scanned that day). LIMITS: history depth is bounded by the 60-day snapshot TTL and starts from when snapshotting was enabled; decay numbers come from daily closes of edge_pp_net (net of default slippage AND Polymarket's own taker fee — see polymarket_edges), not intraday.

{"days":14,"window":"1wk"}

Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price. Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets. FIVE MODEL FAMILIES grouped into three response segments under by_segment: (1) MODEL_DRIVEN — crypto_price (lognormal barrier from 90d FRED log-returns) and news_momentum (GDELT 7d/21d article-volume ratio, soft signal w/ halved Kelly). (2) STRUCTURAL_ARBITRAGE — partition_overround on mutually-exclusive events; per-leg favorite-longshot bias correction with per-sport α (tennis 1.02, soccer 1.10, MMA 1.15, default 1.0); placeholder-slug filter drops will-person-X / will-team-Y / will-manager-Z / will-someone-else- backstops; partitions with >20% placeholder fraction skipped entirely. (3) CONCENTRATED_LONGSHOT — basket trade when one leg ≥75% AND ≥2 longshots ≤8% AND portfolio return ≥25:1; rare-by-design (gates relaxed Run 8 from prior 85%/5%/50:1). EVERY OPPORTUNITY carries edge_pp_net (net of slippage AND Polymarket's own taker fee — fees_pp_applied itemises the fee component; see fees.ts for the published per-category schedule), kelly_fraction + kelly_fraction_half (capped at 0.25), market.liquidity, market.spread_pp, market.volume, plus a 24h-move warning ("Market moved X.Xpp in 24h") when the recent move alone exceeds the edge — your edge may already be in the price. TRADEABLE-EDGE KNOBS: min_liquidity / max_spread_pp drop opportunities where edge isn't realizable; min_partition_leg_kelly filters partitions by best per-leg Kelly. RESPONSE TOP-LEVEL: by_segment{model_driven,structural_arbitrage,concentrated_longshot}, fed_candidates/fed_note (Fed bets surface here, excluded from ranking — 1m-T vs EFFR signal is unreliable at meeting-month horizons without paid OIS/SOFR-futures data), and _diagnostics{concentrated_longshot:{...funnel counters},category_counts,filter_skips} so callers can see WHY a segment is empty (top-N stale, all candidates failed gates,…

{"limit":5,"window":"1wk"}

Realizable-vs-theoretical edge check against live CLOB order-book depth — AND Polymarket's own taker fee, which is never zero and is folded in rather than left for you to add (fleet #3446: r/PredictionsMarkets flagged a "clean" fill that quietly omitted it as optimistic in the direction that loses money). REQUIRES one of `market` (single-market mode) or `event` (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, verdict (clean|degraded|cannot_fill), fee_schedule ({category, taker_rate, exponent, maker_rebate_rate, fees_enabled, basis} — read off THIS market, never assumed free), fee_usd/fee_pp at the actual VWAP fill, breakeven_win_rate_taker (the win probability your side needs just to cover price + fee — ~51.8% on a 50c crypto taker fill, not 50%), breakeven_win_rate_maker (no fee charged to makers — any rebate is in fee_schedule.maker_rebate_rate), and all_in_cost_pp = slippage_pp + fee_pp. BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio and profit_usd — both NET of every leg's own Polymarket taker fee (capture_ratio_gross/profit_usd_gross keep the pre-fee figures; per_leg[].fee_usd itemizes each leg, summed as fees_usd_total), per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and…

{"market":"will-the-fed-increase-interest-rates-by-25-bps-after-the-december-2026-meeting-20260729232808636","side":"buy_yes","size_usd":1000}

Cross-venue spread between Kalshi and Polymarket for the same resolving question. The two venues sometimes price the same outcome 2-25pp apart because their participant pools differ — when the bet shapes are equivalent that delta is a real signal, when they aren't the tool says so. TWO MODES: (1) `topic` — 11 pre-mapped macro subjects ("fed", "btc", "eth", "cpi", "gdp", "sp500", "recession", "next_pope", "next_uk_pm", "next_israel_pm", "2028_president") auto-fetch the matching event on each venue. You do NOT have to use those exact keys: the topic is resolved through aliases and keywords, so "bitcoin", "fed rate decision", "inflation", "s&p 500" and "next pope" all land on the right subject, and `resolution.topic_matched_by` tells you whether it was an exact key, a known alias, a phrase found inside a longer question, or a single-keyword guess — treat "phrase" and "token" as a GUESS at what you meant. An unresolvable topic returns error:"mapping_failed" with mapping_stage:"topic_unrecognized" and known_topics[]; it never silently falls back to a default subject. (2) explicit `kalshi_event_ticker` + `polymarket_event_slug` for custom pairings — BOTH modes run the identical token-overlap matcher, so the same disclosures apply to both. `resolution` is returned in BOTH modes and says how each side's identifier was picked (which Kalshi series was queried, how many events came back, whether the chosen one had quoted markets; which Polymarket search query ran and why that event won). Fleet #2064: when two Polymarket candidates tie on resolution time `polymarket_selected_by` now SAYS so, names every tied slug, names the tie-break that actually decided it (the candidate whose metric_type matches the Kalshi series, else lexicographic slug order), and states whether the winner's metric matches the Kalshi series — it used to assert "picked the soonest-resolving" byte-identically on calls that returned DIFFERENT events, because the tie was settled by upstream fetch arrival orde…

{"topic":"fed"}

{"topic":"btc"}

{"topic":"bitcoin"}

{"topic":"fed rate decision"}

{"topic":"btc 15m"}